基于粒子滤波的约束目标被动跟踪研究

为提高约束条件下的二维机动目标被动跟踪性能,提出了一种约束下的粒子滤波方法(CPF).使用转弯率概念建立了被动跟踪模型,阐述了非线性粒子滤波的基本过程;通过设定地域和机动性能约束条件,抛弃约束外粒子,并对粒子分布和权重进行调整;利用CPF进行了目标跟踪仿真实验,与无约束的粒子滤波跟踪进行了对比,分析了轨迹跟踪性能,比较了跟踪误差.仿真结果表明,CPF能够稳定跟踪,并且具有更高的跟踪精度.

Constraint Target Passive Tracking Based on Particle Filter

To improve the passive tracking performance of 2D maneuvering target,a new constraint particle filter(CPF) is proposed. The passive tracking dynamics is modeled by the turn rate,and the basic steps of the nonlinear particle filter are discussed.According to constraint conditions including the terrain data and the maneuvering capability of the target,outrange particles are discarded during prediction and update steps,and the distribution and the weight of particles are adjusted.The performance and the RMSE are analyzed by comparison simulations with the unconstraint particle filter(UPF). The results show that the CPF is able to track the target stably and is more accurate than the UPF.